Multiscale and Hierarchical Classification for Benthic Habitat Mapping

Geosciences

Mary Young¹  Peter Porskamp¹  Daniel Ierodiaconou¹  Alex Rattray¹ 

[1] Centre for Integrative Ecology, School of Life and Environmental Sciences, Deakin University, P.O. Box 423, Warrnambool, VIC 3280, Australia;

关键词: Multibeam bathymetry;    benthic habitat mapping;    multiscale;    Random Forests;   

DOI  :  10.3390/geosciences8040119

来源: DOAJ

【 摘 要 】

Developing quantitative and objective approaches to integrate multibeam echosounder (MBES) data with ground observations for predictive modelling is essential for ensuring repeatability and providing confidence measures for benthic habitat mapping. The scale of predictors within predictive models directly influences habitat distribution maps, therefore matching the scale of predictors to the scale of environmental drivers is key to improving model accuracy. This study uses a multi-scalar and hierarchical classification approach to improve the accuracy of benthic habitat maps. We used a 700-km2 region surrounding Cape Otway in Southeast Australia with full MBES data coverage to conduct this study. Additionally, over 180 linear kilometers of towed video data collected in this area were classified using a hierarchical classification approach. Using a machine learning approach, Random Forests, we combined MBES bathymetry, backscatter, towed video and wave exposure to model the distribution of biotic classes at three hierarchical levels. Confusion matrix results indicated that greater numbers of classes within the hierarchy led to lower model accuracy. Broader scale predictors were generally favored across all three hierarchical levels. This study demonstrates the benefits of testing predictor scales across multiple hierarchies for benthic habitat characterization.

【 授权许可】

Unknown